Plasmacytoid dendritic cells (pDCs) specialize in the production of type I interferons (IFNs) and promote antiviral immune responses following engagement of pattern recognition receptors. They have been mainly implicated in the pathogenesis of autoimmune diseases that are characterized by a type I IFN signature (notably, IFN-α). Yet, pDCs have also been shown to be able to induce tolerogenic immune responses (Di Domizio and Cao, 2013; Swiecki and Colonna, 2015; Sisirak et al., 2014; Rönnblom and Pascual, 2008). Due to the clinical significance of pDCs, several studies aimed at mapping pDC lineage derivation have emerged recently. Yet, a complete understanding of pDC development and their existing subsets in mice is still lacking. The transcription factor 4 (TCF4) is required for pDC development and for lineage identity (Cisse et al., 2008; Ghosh et al., 2010; Reizis et al., 2011). TCF4 is a component of a multiprotein complex that includes both positive and negative regulators (Swiecki and Colonna, 2015; Grajkowska et al., 2017). One of these components, the transcription factor BCL11a, which is also essential for B cell development (Liu et al., 2003; Ippolito et al., 2014; Yu et al., 2012), induces Tcf4 transcription and initiates a positive feedback loop with TCF4 to maintain pDC lineage commitment and function (Ippolito et al., 2014; Yu et al., 2012; Wu et al., 2013).

Unlike their conventional dendritic cell (cDC) counterparts, pDCs express transcriptional regulators and markers associated with B-lymphocyte development in addition to BCL11a (e.g. B220, SPIB) (Pelayo et al., 2005; Sathe et al., 2013). These features, along with the established generation of pDCs from myeloid restricted precursors (Feng et al., 2022; Harman et al., 2006), have made it difficult to define pDC lineage affiliation (Reizis et al., 2011; Pelayo et al., 2005; Shigematsu et al., 2004; Wang and Liu, 2004). This has led to the hypothesis that pDC subsets may have distinct origins derived from either the common lymphoid progenitor (CLP) or the common myeloid progenitor (CMP) (Sathe et al., 2013; Nikolic et al., 2002). Beyond the complex nature of their lineage, pDCs with different functional attributes (e.g. variable IFN-α expression levels) or different surface markers (e.g. CD19⁺ pDCs detected in tumor-draining lymph nodes) have also been identified (Pelayo et al., 2005; Yang et al., 2005; Mellor et al., 2005; Munn et al., 2004).

On par with the ever-increasing heterogeneity described within pDC populations, a novel AXL⁺ dendritic cell (DC) population with many pDC-like properties has recently been discovered in human blood (Villani et al., 2017; Alcántara-Hernández et al., 2017; Matsui et al., 2009). While AXL⁺ DCs express many canonical pDC protein markers (e.g. CD123, BDCA2/CD303), they also express CD2, the Ig-like lectins SIGLEC1 and SIGLEC6, as well as the activation marker CD81 (Villani et al., 2017). In a separate study (Zhang et al., 2017), a similar population of pDCs was shown to express high levels of CD5 and CD81 in mice and humans, two glycoproteins normally associated with the B cell receptor (BCR) signaling complex. The origin of AXL⁺ transitional DCs is currently unclear and the presence of a homologous population in mice remains muddled. Here, we report the identification of a lymphoid-derived pDC subset (B-pDCs) that shares inflammatory features with myeloid-derived Axl⁺ pDC populations in mice. We also demonstrate an in vivo requirement for Bcl11a in the transcriptional specification of B-pDCs.

We previously demonstrated that conditional deletion of Bcl11a in the hematopoietic stem cell compartment mediated by Vav1-Cre or by inducible Mx1-Cre recombinase results in complete abolishment of pDC development (Ippolito et al., 2014). Spurred by previous speculation of pDC origin from the CLP (Pelayo et al., 2005; Wang and Liu, 2004), we next selectively deleted floxed (^F) Bcl11a alleles in Cd79a⁺ cells (Hobeika et al., 2006; Sakaguchi et al., 1988), as mediated by Cd79a-Cre in vivo. Expression of the Cd79a gene (Cd79a) initiates at the LY6D⁺ CLP stage, in B-cell-biased lymphoid progenitors (BLPs) (Inlay et al., 2009), downstream of LY6D^– CLPs (Figure 1—figure supplement 1). Bcl11a^F/FCd79a-Cre mice (cKO) and littermate controls were examined for pDC frequencies among nucleated cells in the bone marrow (BM). B220⁺ PDCA1⁺ pDCs (all found to be CD11c ^int) were consistently and significantly reduced by an average of ~25% (24.8 ± 2.4%) in cKO mice relative to littermates (Figure 1A and B). Loss of B cells (B220^hi PDCA1^-) served as a gauge of Cd79a-Cre deletion efficiency (Figure 1A and B). Taken together, these data indicate that a significant proportion of pDCs are derived from BLPs or BLP-derived cells and are Bcl11a dependent.

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To expand these in vivo observations, and to confirm that this defect is intrinsic to hematological progenitor cells, we transferred BM from either BCL11A-sufficient reporter control mice (Cd79a-Cre-YFP) or BCL11A-deficient cKO mice (Bcl11a^F/FCd79a-Cre-YFP) into lethally irradiated wildtype C57BL/6J recipients. After 8 weeks, <10% of B cells (B220⁺ PDCA1^-) in the spleens of Cd79a-Cre-YFP recipients were YFP^–, confirming elimination of recipient hematopoiesis (Figure 1D and E). As expected, Bcl11a^F/FCd79a-Cre-YFP BM resulted in significantly reduced B cell and pDC cellularity compared to Cd79a-Cre-YFP controls, whereas BCL11A-sufficient (YFP^–) pDCs persisted (Figure 1C, E, and F).

Approximately one-third of pDCs in the spleen of wildtype chimeras were YFP⁺ compared to only one-fifth in BM (Figure 1G). This increased fraction of YFP⁺ pDCs in the spleen suggests that BLP-derived pDCs preferentially home to that organ. While the residual YFP⁺ pDCs in the BM might be an indication that Bcl11a is not shut off at the most precise developmentally critical point within the B cell lineage for complete Cd79a⁺ pDC ablation, other hematopoietic lineages were capable of development in normal numbers and contained a paucity of YFP⁺ cells (Figure 1—figure supplement 2). Of note, BCL11A-deficient progenitors yielded higher splenic T cell chimerism at the expense of B cells and pDCs, but less than 2% of T cells were YFP⁺ (Figure 1—figure supplement 2, and not shown). This indicated that Cd79a-cre expression occurs subsequent to T-B lineage divergence, consonant with the previous observations (Inlay et al., 2009; Nagaharu et al., 2022; Herman and Grün, 2018) and our Cd79a-cre progenitor analysis in which YFP⁺ cells are confined to the BLP-derived compartment (Figure 1—figure supplement 1). Because of their exclusive lymphoid derivation post T-B bifurcation, we were prompted hereafter to refer to this pDC lineage as ‘B-pDC’.

Relative to Cd79a^- pDCs, resting B-pDCs express higher levels of MHC Class II (Figure 2A and B), suggesting that they may be primed for immediate response to pro-inflammatory signals. To test this hypothesis, we delivered TLR9 ligand (CpG:ODN) into Cd79a-Cre-YFP mice via tail vein injection, and splenic pDCs were phenotyped via flow cytometry 24 hr later. While both pDC and B-pDC compartments expanded relative to controls (Figure 2C and D), the YFP⁺ B-pDC fraction increased almost twofold above that of the YFP^– pDC fraction in numbers (Figure 2E). Additionally, fold increase of B-pDCs coexpressing CD83 and CD86 upon activation was also significantly higher than that of YFP^- pDCs (Figure 2F and G).These results suggested that relative to YFP^- pDCs, B-pDCs are intrinsically activated and primed for rapid expansion upon TLR9 engagement. To confirm the functional phenotype of B-pDCs, we tested their ability to secrete cytokines known to be elicited by pDCs after Toll-like receptor (TLR) engagement. Specifically, we tested each pDC lineage for the production of IFN-α or IL-12p40 when activated by TLR9-bound CpG oligonucleotides. We sorted B-pDCs and pDCs, engaged TLR9 with CpG:ODNs for 24 hr and collected supernatant for cytokine-specific ELISAs. IFN-α production was almost negligible in B-pDC (Figure 2H), yet IL-12p40 production was significantly augmented over pDC (Figure 2H). To test their ability to expand T lymphocytes in culture, sorted B-pDCs and pDCs were incubated with CpG:ODNs and co-cultured with freshly isolated CFSE-labeled lymphocytes. After 6 days, co-cultures were stained for CD3 and CFSE-negative cell percentages were recorded (Figure 2I and J). Altogether, our results showed B-pDCs were significantly better at expanding T cells in co-cultures than conventional pDCs (Figure 2I and J) upon TLR9 activation.

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To further elucidate their phenotype at the genetic level, we performed RNA-seq analyses of purified B-pDCs and compared them to classical, myeloid-derived bulk pDCs. While the overall gene expression patterns were highly similar across the two subsets,~1% of transcripts (~220 genes) differed significantly (q-value<0.05) (Figure 2—figure supplement 1). Among the top overexpressed genes in B-pDCs were Lyz1, Ccr3, Cd86, Id2, Axl, Siglec1, and Cd81 (Figure 2—figure supplement 1). Differentially expressed transcripts generated Gene Ontology (GO) (Huang et al., 2009) or Panther (Thomas et al., 2003) terms including ‘immune response’, ‘inflammatory response’, ‘cell activation’, and ‘regulation of immune response’ (p=1.36 × 10^–17, 2.15×10^–12, 3.67×10^–11, and 1.45×10^–10), respectively (Figure 2—figure supplement 1). Gene set enrichment analysis (GSEA) revealed elevation of each of these same GO/Panther terms within the B-pDC subset as compared to a pDC-related GSEA control dataset that showed no enrichment. Next, we compared all genes expressed by both pDC populations to one another and to published (Liu et al., 2014) RNA-seq of BM-derived mouse pre-B cells (B220⁺ IgM^- Kit^- CD25⁺)—a post-CLP B cell progenitor (pre-B) population. As shown in Figure 2D, pDCs and B-pDCs expression levels were strongly correlated with one another relative to early B cells (R² values = 0.8959, 0.4145, and 0.404, respectively) (Figure 2—figure supplement 1). Collectively, these data support our contention that B-pDCs are functionally distinct from classical pDCs and specialize in inflammatory responses, antigen presentation, and T cell activation.

AXL⁺ transitional DCs (tDCs or AS-DCs) Villani et al., 2017; Leylek et al., 2019 have been identified in humans by the high expression levels of the genes AXL, CD81, CD86, LYZ2, C1QA, CD2, and C1QB relative to classical pDCs (Villani et al., 2017; Zhang et al., 2017; Alcántara-Hernández et al., 2017; Matsui et al., 2009). They also exhibit many classical pDC features, having previously been described phenotypically as a ‘continuum’ between pDC and cDC2 populations (Zhang et al., 2017). In order to confirm AXL⁺ pDCs are also present in mice and corroborate our RNA-seq findings, we phenotyped B-pDCs through imaging flow cytometry. First, we again verified that our primary gating scheme effectively encompassed bona fide pDCs using imaging flow cytometry. As expected, B220⁺ PDCA1^- cells were >95% CD19⁺ SIGLEC-H^- (B cells). Most importantly, >95% of B220⁺ PDCA1 ^hi cells were SIGLEC-H ⁺and CD19^-, ruling out any pDC population contamination (Figure 2—figure supplement 2). At the protein level, we were able to stain mouse BM cells with anti-AXL antibodies without issues and confirmed that while ~60% of B-pDCs were positive for AXL, only ~10% of YFP negative pDCs expressed this protein (Figure 2—figure supplement 2). Finally, imaging of BM cells showed that B-pDCs resembled classical secretory pDCs morphologically, presenting a large nucleus and defined plasma-like features, in contrast to B cells (Figure 2—figure supplement 2). This preliminary phenotypic analysis led us to speculate that similarly to human AXL⁺ transitional DCs, B-pDC might also prioritize their secretory capacity for copious transcription and secretion of alternative immune system modulators. Thus, we performed high-resolution 10× single-cell RNA-seq analyses of magnetically sorted PDCA1⁺ BM cells (96% purity as determined by flow cytometry) and compared the B-pDC transcriptional profile to that of B cells and other pDCs. Principal component-based clustering and UMAP visualization of sorted PDCA⁺ cells yielded 22 discrete clusters. To identify cell populations in an unbiased manner, we uploaded the top 1000 differentially expressed genes (DEGs) from each cluster into CIPR (Cluster Identity Predictor) (Ekiz et al., 2020), which uses the reference database ImmGen (mouse) to calculate cell identity scores (Supplementary file 1). Seurat clusters were named according to the consensus of the top 5 cell ID hits generated by CIPR (Figure 3A). We confirmed CIPR assignments by evaluating cluster DEGs and expression of markers associated with specific myeloid cell populations (Figure 3—figure supplement 1). PDCA1⁺ cells comprised nine distinct pDC populations (including B-pDCs, which were unanimously classified as pDCs by CIPR identity scores despite its unique Cd79a expression). Of note, pDC8 had one of the lowest ID scores among consensus pDCs (Supplementary file 1) and yielded CIPR identity hits for both pDCs and ‘CMPs’ within its top 5 ID hits (not shown). This suggested that this single cluster (1.7% of total cells) represented a mixed population of pDCs and differentiating CMPs expressing high levels of Hbb family genes (hemoglobin associated genes) under the chosen default Seurat clustering resolution settings. Because of pDC8 mixed identity, we excluded this cluster from our further single-cell transcriptional analysis. Besides pDCs, a small B cell cluster (2.75% of all cells analyzed) expressing low levels of Bst2 (PDCA1 gene), as well as distinct subpopulations of macrophages (Mac1, -2, and -3), monocytes (Mono1, -2, and -3), and granulocytes (Granul1, -2, and -3) were also identified. Two stem-progenitor populations (Prog1 and -2) were present in our cell pool (1.12% and 2.86% of cells analyzed, respectively) (Figure 3A and B). The Prog2 population expressed high levels of genes associated with pDCs, including Siglech, Bst2, Flt3, TCF4, and Irf8 and had a ‘CDC’ (common dendritic cell progenitor) signature according to its CIPR reference ID (Supplementary file 2). In addition, the majority of cells in the Prog2 cluster were Csf1r^-, Il7r⁺, Siglech⁺, Ly6d⁺ (Figure 3—figure supplement 1, Supplementary file 2), resembling the pro-pDC myeloid precursor population described by Rodriguez et al., as well as Dress and colleagues (Rodrigues et al., 2018; Dress et al., 2019). In contrast, Prog1, which clustered near B cells and was identified as ‘MLP’ (multi-lymphoid progenitors entering the CLP stage), expressed several pre/pro B cell genes, including Jchain, Iglc1, Iglc2, Igha, Igkc (Supplementary file 2). Direct DEG comparison between Prog1 and Prog2 clusters confirmed Prog1 cells exhibit a more ‘Pre/Pro B cell-like’ developmental profile and low expression of markers that are ‘pDC-biased’ relative to Prog2 (Supplementary file 2). After clustering and profiling PDCA1⁺ cells, we subsetted the data in order to compare B-pDCs gene expression to only B cell and pDC clusters using Wilcoxon rank sum tests. We plotted several genes commonly associated with B cells and pDCs. Except for Cd79a and Cd79b, B-pDCs showed negligible expression of mature B cell associated genes such as Pax5, Cd19, and Cd22 (Figure 3C). Expression of canonical pDC genes was present but reduced in B-pDCs relative to classical pDC subsets (Figure 3D). Our analysis highlights the B-pDC cluster uniqueness among pDC subsets and further supported its lineage divergence from mature B cells.

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In order to temporally examine lymphoid transcriptional commitment to B cell or B-pDC lineages, we inferred developmental trajectories for B and B-pDC single cells. Pseudotime is a measure of how much progress an individual cell has made through a process such as differentiation (Qiu et al., 2017; Cao et al., 2019). In other words, cells that belong to a single cluster can be segregated across various transcriptional ‘states’ based on number of reads per cell of a particular set of developmental genes. Although we cannot say the pre/pro B cell-like Prog1 cluster represents an immediately adjacent common progenitor for B cells and B-pDCs, we confirmed B-pDCs are not a direct precursor for B cells based on their single-cell developmental trajectory. Cells in the Prog1 cluster were chosen as state ‘0’ based on their abundant expression of lymphoid progenitor associated genes (Figure 4A). Analysis of uniquely expressed genes showed Prog1 downregulated several multipotency markers such as Stmn1, Gata2, and Ctla2a, while B cells increased expression of transcription factors needed for B cell commitment (e.g. Pax5, Ebf1, and Ms4a1) (Figure 4B and C). Although B-pDCs did not express detectable levels of most B cell commitment markers evaluated, they did retain expression of early BCR associated genes, including Ly6d, Ighm, Igkc, Iglc2, and Jchain (Figure 4—figure supplement 1). Because these early B cell-related markers are also expressed in plasma cells, we performed flow cytometric analysis of B220⁺ PDCA1⁺:Cd79a⁺ cells. We discovered that B-pDCs express negligible levels of TACI (a transcription factor that silences the B cell developmental program; Shapiro-Shelef and Calame, 2005), and that expression of CD138 and IgG1 was also negligible (Figure 4—figure supplement 1, and not shown), thus ruling out any major plasma cell overlap at the protein level. Lineage analysis also revealed that B-pDCs enhanced expression of the pDC associated markers Siglech, Tcf4, Bst2, and also of Xbp1 (a marker expressed at high levels in secretory cells). Committed B cells in turn downregulated these markers as they progressed through pseudotime (Figure 4D). In summary, lineage analyses revealed that as B-pDCs develop, they retain expression of both early BCR genes and pDC associated genes, diverging from B cells through continued suppression of transcription factors required for terminal B cell differentiation.

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In order to determine if murine B-pDCs are homologous with AXL⁺ human DCs, we subsetted pDCs clusters and plotted their relative expression of AXL⁺ DC associated genes. We identified B-pDCs and pDC5 as ‘noncanonical pDCs’ due to their distinguishing high expression of Axl (Figure 5A). Accordingly, Axl negative pDCs are referred hereafter as ‘classical pDCs’. We then analyzed the top DEGs shared between B-pDCs and pDC5s using the function ‘FindConservedMarkers’. Lyz2, the complement genes C1qa, C1qb, C1qc, and Cd5l, were the top conserved markers for these two pDC subsets (Figure 5A). Of note, Cd5l, a macrophage associated secreted protein, has been shown to play a major role in initiating inflammation and maintaining humoral responses (Sanjurjo et al., 2015; Martinez et al., 2014; Oskam et al., 2023). Altogether, our analysis revealed that constitutive expression of Axl and innate activation markers are present in more than one transcriptionally distinct murine pDC profile, stressing the functional heterogeneity and plasticity of murine pDCs.

Figure 5

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Noncanonical AXL⁺ DCs in humans have been shown to be derived from a myeloid pro-pDC progenitor (Dress et al., 2019; Herman and Grün, 2018; Rodrigues et al., 2018). In mice, Feng and colleagues used clonal lineage to show that pDCs and cDCs originate from a common Flt3-dependent pathway of differentiation originating from transient CX3CR1⁺ progenitors. Since the authors state that their finding does ‘not completely rule out the common origin of pDCs and B cells, which may bifurcate earlier in hematopoiesis’, we set out to corroborate the hematopoietic origins of pDC5 and B-pDCs. We first calculated pDC5’s ‘community connectedness’ (Wolf et al., 2019) to other pDC clusters and plotted their developmental trajectories originating from the pro-pDC progenitor cluster Prog2 using Monocle3 (Trapnell et al., 2014). Partitioning Prog2 and pDC single cells into Louvain communities yielded five supergroups, with the largest group encompassing most pDC clusters, including pDC5. The other groups consisted of non-pDCs (Prog2 cells), B-pDCs, and a few single cells (>20 cells) genotypically distinct from their original pDC clusters (Figure 5B). Ultimately, this statistical grouping confirmed that B-pDCs were not developmentally linked to ‘classical’ AXL negative pDCs or Axl⁺ pDC5 cells. Accordingly, B-pDCs did not integrate into the predicted lineage trajectory originating from Prog2, whereas the Axl⁺ pDC5 population did (Figure 5B). Both B-pDCs and pDC5s exhibited a similar late developmental pseudotime, while clusters pDC6 and pDC4 were the first to develop from Prog2. Collectively, our data establish that murine noncanonical pDCs (AXL⁺) consist of at least two major pDC subpopulations, which develop from lymphoid and myeloid progenitors, respectively.

Dendritic cell-subset biology, development, and the ensuing nomenclature have long been unclear and everchanging. Here, we provide definitive evidence in support of the long-suspected ‘lymphoid past’ of pDCs by establishing their ability to arise in vivo from CLP-derived progenitors with B/pDC bipotential lineage capacity (Pelayo et al., 2005; Sathe et al., 2013; Shigematsu et al., 2004; Björck and Kincade, 1998; Izon et al., 2001). Our data shows that the murine pDC compartment is bipartite, being comprised of B-pDCs—diverted from the CLP post T-B bifurcation—as well as myeloid-derived classical pDCs. Unlike most myeloid-derived pDCs, B-pDCs constitutively express high levels of multiple innate activation markers, including C1qa, C1qb, Lyz2, and Cd81. Functionally, they expand more readily after TLR9 engagement than classical pDCs (either through increased proliferation or differentiation of other cell types) and excel at activating T cells in culture. While further functional definition awaits discovery, our work provides a framework for the identification and segregation of the B-pDC lineage (comprising almost one-fifth of the total pDC compartment) from other myeloid-derived pDC subpopulations. Primarily, our observations support the hypothesis that DC functionality derives primarily from ontogeny rather than from tissue environment (Heidkamp et al., 2016), exemplified by evolution of a specialized pDC lineage from a lymphoid progenitor. In the case of the B-pDCs, our data suggest such a cell may deviate from B cell commitment after Ly6d expression, thought to be the earliest marker of B cell specification (Inlay et al., 2009).

Most noticeably, our newly described B-pDC population expresses high levels of the tyrosine kinase AXL and specializes in T cell activation through displaying high levels of MHC II and costimulatory markers. Of note, ablation of AXL in mice has previously been shown to increase expression of type I IFN while impairing IL-1β production and T cell activation during viral infections (Schmid et al., 2016; Zhang et al., 2022). Our related findings that AXL⁺ B-pDCs excel at T cell priming but exhibit reduced IFNα expression might suggest these DCs play an important role in advancing T cell expansion, while curbing innate immune responses that can result in autoimmune damage. Of note, whether induction of IFN-I production in vivo could also affect CLP and increase the amount of YFP⁺ lymphoid progenitors and thus B-pDC output is unclear. Further research is required to answer this question.

In addition to describing a novel B cell-like pDC population, we profiled murine can develop in parallel from either myeloid or lymphoid progenitors, with lymphoid-derived B-pDCs retaining expression of additional early B cell genes that are not expressed in myeloid-derived pDCs. Intriguingly, ChIP-seq analysis done by our lab of BCL11A target binding in multiple human cell lines suggests that an evolutionarily conserved transcriptional hierarchy might distinguish AXL⁺ pDCs and B cells in humans as well (unpublished data). The demarcation of lymphoid derived B-pDCs as one of the AXL⁺ pDC populations found in mice may help clarify the perceived plasticity of the pDC compartment in normal and disease contexts (Li et al., 2017), as well as provide a new cell for targeted study within the context of autoimmune disease, cancer, and infection models.

RNA-seq: GSE105827; ChIP-seq: GSE99019; 10x ScRNA-seq: GSE225768. Accession Numbers to previously published data sets: GSE52868 (pre-B RNA-seq).

1. 1. Araujo AM
   2. Dekker JD
   3. Garrison K
   4. Su Z
   5. Rhee C
   6. Hu Z
   7. Lee BK
   8. Hurtado DS
   9. Lee J
   10. Iyer VR
   11. Elrlich LIR
   12. Georgiou G
   13. Ippolito GC
   14. Stephen YS
   15. Tucker HO

   (2018) NCBI Gene Expression Omnibus

   ID GSE105827. Common lymphoid progenitor derivation of plasmacytoid dendritic cells is mediated by Bcl11a.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE105827
2. 1. Araujo AM
   2. Dekker JD
   3. Garrison K
   4. Su Z
   5. Rhee C
   6. Hu Z
   7. Lee BK
   8. Hurtado DO
   9. Lee J
   10. Iyer VR
   11. Ehrlich LIR
   12. Georgiou G
   13. Ippolito GC
   14. Stephen YS
   15. Tucker HO

   (2017) NCBI Gene Expression Omnibus

   ID GSE99019. Common lymphoid progenitor derivation of plasmacytoid dendritic cells is mediated by Bcl11a.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE99019
3. 1. Araujo AM
   2. Dekker JD
   3. Garrison K
   4. Su Z
   5. Rhee C
   6. Hu Z
   7. Lee BK
   8. Hurtado DO
   9. Lee J
   10. Iyer VR
   11. Ehrlich LIR
   12. Georgiou G
   13. Ippolito GC
   14. Stephen YS
   15. Tucker HO

   (2023) NCBI Gene Expression Omnibus

   ID GSE225768. Common lymphoid progenitor derivation of plasmacytoid dendritic cells is mediated by Bcl11a.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE225768

1. 1. Liu G
   2. Hu Y
   3. Smyth GK
   4. Dickins R

   (2014) NCBI Gene Expression Omnibus

   ID GSE52868. Expression profiling of mouse bone marrow pre-B cells.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52868

1. 1. Alcántara-Hernández M
   2. Leylek R
   3. Wagar LE
   4. Engleman EG
   5. Keler T
   6. Marinkovich MP
   7. Davis MM
   8. Nolan GP
   9. Idoyaga J

   (2017) High-dimensional phenotypic mapping of human dendritic cells reveals interindividual variation and tissue specialization

   Immunity 47:1037–1050.

   https://doi.org/10.1016/j.immuni.2017.11.001

   • PubMed
   • Google Scholar
2. 1. Björck P
   2. Kincade PW

   (1998)

   CD19+ pro-B cells can give rise to dendritic cells in vitro

   Journal of Immunology 161:5795–5799.

   • PubMed
   • Google Scholar
3. 1. Cao J
   2. Spielmann M
   3. Qiu X
   4. Huang X
   5. Ibrahim DM
   6. Hill AJ
   7. Zhang F
   8. Mundlos S
   9. Christiansen L
   10. Steemers FJ
   11. Trapnell C
   12. Shendure J

   (2019) The single-cell transcriptional landscape of mammalian organogenesis

   Nature 566:496–502.

   https://doi.org/10.1038/s41586-019-0969-x

   • Google Scholar
4. 1. Cisse B
   2. Caton ML
   3. Lehner M
   4. Maeda T
   5. Scheu S
   6. Locksley R
   7. Holmberg D
   8. Zweier C
   9. den Hollander NS
   10. Kant SG
   11. Holter W
   12. Rauch A
   13. Zhuang Y
   14. Reizis B

   (2008) Transcription factor E2-2 is an essential and specific regulator of plasmacytoid dendritic cell development

   Cell 135:37–48.

   https://doi.org/10.1016/j.cell.2008.09.016

   • PubMed
   • Google Scholar
5. 1. Di Domizio J
   2. Cao W

   (2013) Fueling autoimmunity: type I interferon in autoimmune diseases

   Expert Review of Clinical Immunology 9:201–210.

   https://doi.org/10.1586/eci.12.106

   • PubMed
   • Google Scholar
6. 1. Dress RJ
   2. Dutertre CA
   3. Giladi A
   4. Schlitzer A
   5. Low I
   6. Shadan NB
   7. Tay A
   8. Lum J
   9. Kairi M
   10. Hwang YY
   11. Becht E
   12. Cheng Y
   13. Chevrier M
   14. Larbi A
   15. Newell EW
   16. Amit I
   17. Chen J
   18. Ginhoux F

   (2019) Plasmacytoid dendritic cells develop from Ly6D⁺ lymphoid progenitors distinct from the myeloid lineage

   Nature Immunology 20:852–864.

   https://doi.org/10.1038/s41590-019-0420-3

   • PubMed
   • Google Scholar
7. 1. Ekiz HA
   2. Conley CJ
   3. Stephens WZ
   4. O’Connell RM

   (2020) CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments

   BMC Bioinformatics 21:191.

   https://doi.org/10.1186/s12859-020-3538-2

   • PubMed
   • Google Scholar
8. 1. Feng J
   2. Pucella JN
   3. Jang G
   4. Alcántara-Hernández M
   5. Upadhaya S
   6. Adams NM
   7. Khodadadi-Jamayran A
   8. Lau CM
   9. Stoeckius M
   10. Hao S
   11. Smibert P
   12. Tsirigos A
   13. Idoyaga J
   14. Reizis B

   (2022) Clonal lineage tracing reveals shared origin of conventional and plasmacytoid dendritic cells

   Immunity 55:405–422.

   https://doi.org/10.1016/j.immuni.2022.01.016

   • PubMed
   • Google Scholar
9. 1. Ghosh HS
   2. Cisse B
   3. Bunin A
   4. Lewis KL
   5. Reizis B

   (2010) Continuous expression of the transcription factor e2-2 maintains the cell fate of mature plasmacytoid dendritic cells

   Immunity 33:905–916.

   https://doi.org/10.1016/j.immuni.2010.11.023

   • PubMed
   • Google Scholar
10. 1. Grajkowska LT
    2. Ceribelli M
    3. Lau CM
    4. Warren ME
    5. Tiniakou I
    6. Nakandakari Higa S
    7. Bunin A
    8. Haecker H
    9. Mirny LA
    10. Staudt LM
    11. Reizis B

    (2017) Isoform-specific expression and feedback regulation of E protein TCF4 control dendritic cell lineage specification

    Immunity 46:65–77.

    https://doi.org/10.1016/j.immuni.2016.11.006

    • PubMed
    • Google Scholar
11. 1. Harman BC
    2. Miller JP
    3. Nikbakht N
    4. Gerstein R
    5. Allman D

    (2006) Mouse plasmacytoid dendritic cells derive exclusively from estrogen-resistant myeloid progenitors

    Blood 108:878–885.

    https://doi.org/10.1182/blood-2005-11-4545

    • PubMed
    • Google Scholar
12. 1. Heidkamp GF
    2. Sander J
    3. Lehmann CHK
    4. Heger L
    5. Eissing N
    6. Baranska A
    7. Lühr JJ
    8. Hoffmann A
    9. Reimer KC
    10. Lux A
    11. Söder S
    12. Hartmann A
    13. Zenk J
    14. Ulas T
    15. McGovern N
    16. Alexiou C
    17. Spriewald B
    18. Mackensen A
    19. Schuler G
    20. Schauf B
    21. Forster A
    22. Repp R
    23. Fasching PA
    24. Purbojo A
    25. Cesnjevar R
    26. Ullrich E
    27. Ginhoux F
    28. Schlitzer A
    29. Nimmerjahn F
    30. Schultze JL
    31. Dudziak D

    (2016) Human lymphoid organ dendritic cell identity is predominantly dictated by ontogeny, not tissue microenvironment

    Science Immunology 1:eaai7677.

    https://doi.org/10.1126/sciimmunol.aai7677

    • PubMed
    • Google Scholar
13. 1. Herman JS
    2. Grün D

    (2018) FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data

    Nature Methods 15:379–386.

    https://doi.org/10.1038/nmeth.4662

    • PubMed
    • Google Scholar
14. 1. Hobeika E
    2. Thiemann S
    3. Storch B
    4. Jumaa H
    5. Nielsen PJ
    6. Pelanda R
    7. Reth M

    (2006) Testing gene function early in the B cell lineage in mb1-cre mice

    PNAS 103:13789–13794.

    https://doi.org/10.1073/pnas.0605944103

    • PubMed
    • Google Scholar
15. 1. Huang DW
    2. Sherman BT
    3. Lempicki RA

    (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

    Nucleic Acids Research 37:1–13.

    https://doi.org/10.1093/nar/gkn923

    • PubMed
    • Google Scholar
16. 1. Inlay MA
    2. Bhattacharya D
    3. Sahoo D
    4. Serwold T
    5. Seita J
    6. Karsunky H
    7. Plevritis SK
    8. Dill DL
    9. Weissman IL

    (2009) Ly6d marks the earliest stage of B-cell specification and identifies the branchpoint between B-cell and T-cell development

    Genes & Development 23:2376–2381.

    https://doi.org/10.1101/gad.1836009

    • Google Scholar
17. 1. Ippolito GC
    2. Dekker JD
    3. Wang YH
    4. Lee BK
    5. Shaffer AL
    6. Lin J
    7. Wall JK
    8. Lee BS
    9. Staudt LM
    10. Liu YJ
    11. Iyer VR
    12. Tucker HO

    (2014) Dendritic cell fate is determined by BCL11A

    PNAS 111:E998–E1006.

    https://doi.org/10.1073/pnas.1319228111

    • PubMed
    • Google Scholar
18. 1. Izon D
    2. Rudd K
    3. DeMuth W
    4. Pear WS
    5. Clendenin C
    6. Lindsley RC
    7. Allman D

    (2001) A common pathway for dendritic cell and early B cell development

    Journal of Immunology 167:1387–1392.

    https://doi.org/10.4049/jimmunol.167.3.1387

    • PubMed
    • Google Scholar
19. 1. Leylek R
    2. Alcántara-Hernández M
    3. Lanzar Z
    4. Lüdtke A
    5. Perez OA
    6. Reizis B
    7. Idoyaga J

    (2019) Integrated cross-species analysis identifies a conserved transitional dendritic cell population

    Cell Reports 29:3736–3750.

    https://doi.org/10.1016/j.celrep.2019.11.042

    • PubMed
    • Google Scholar
20. 1. Li S
    2. Wu J
    3. Zhu S
    4. Liu YJ
    5. Chen J

    (2017) Disease-associated plasmacytoid dendritic cells

    Frontiers in Immunology 8:1268.

    https://doi.org/10.3389/fimmu.2017.01268

    • PubMed
    • Google Scholar
21. 1. Liu P
    2. Keller JR
    3. Ortiz M
    4. Tessarollo L
    5. Rachel RA
    6. Nakamura T
    7. Jenkins NA
    8. Copeland NG

    (2003) BCL11A is essential for normal lymphoid development

    Nature Immunology 4:525–532.

    https://doi.org/10.1038/ni925

    • PubMed
    • Google Scholar
22. 1. Liu GJ
    2. Cimmino L
    3. Jude JG
    4. Hu Y
    5. Witkowski MT
    6. McKenzie MD
    7. Kartal-Kaess M
    8. Best SA
    9. Tuohey L
    10. Liao Y
    11. Shi W
    12. Mullighan CG
    13. Farrar MA
    14. Nutt SL
    15. Smyth GK
    16. Zuber J
    17. Dickins RA

    (2014) Pax5 loss imposes a reversible differentiation block in B-progenitor acute lymphoblastic leukemia

    Genes & Development 28:1337–1350.

    https://doi.org/10.1101/gad.240416.114

    • PubMed
    • Google Scholar
23. 1. Martinez VG
    2. Escoda-Ferran C
    3. Tadeu Simões I
    4. Arai S
    5. Orta Mascaró M
    6. Carreras E
    7. Martínez-Florensa M
    8. Yelamos J
    9. Miyazaki T
    10. Lozano F

    (2014) The macrophage soluble receptor AIM/Api6/CD5L displays a broad pathogen recognition spectrum and is involved in early response to microbial aggression

    Cellular & Molecular Immunology 11:343–354.

    https://doi.org/10.1038/cmi.2014.12

    • PubMed
    • Google Scholar
24. 1. Matsui T
    2. Connolly JE
    3. Michnevitz M
    4. Chaussabel D
    5. Yu C-I
    6. Glaser C
    7. Tindle S
    8. Pypaert M
    9. Freitas H
    10. Piqueras B
    11. Banchereau J
    12. Palucka AK

    (2009) CD2 distinguishes two subsets of human plasmacytoid dendritic cells with distinct phenotype and functions

    Journal of Immunology 182:6815–6823.

    https://doi.org/10.4049/jimmunol.0802008

    • PubMed
    • Google Scholar
25. 1. Mellor AL
    2. Baban B
    3. Chandler PR
    4. Manlapat A
    5. Kahler DJ
    6. Munn DH

    (2005) Cutting edge: CpG oligonucleotides induce splenic CD19+ dendritic cells to acquire potent indoleamine 2,3-dioxygenase-dependent T cell regulatory functions via IFN Type 1 signaling

    Journal of Immunology 175:5601–5605.

    https://doi.org/10.4049/jimmunol.175.9.5601

    • PubMed
    • Google Scholar
26. 1. Mootha VK
    2. Lindgren CM
    3. Eriksson K-F
    4. Subramanian A
    5. Sihag S
    6. Lehar J
    7. Puigserver P
    8. Carlsson E
    9. Ridderstråle M
    10. Laurila E
    11. Houstis N
    12. Daly MJ
    13. Patterson N
    14. Mesirov JP
    15. Golub TR
    16. Tamayo P
    17. Spiegelman B
    18. Lander ES
    19. Hirschhorn JN
    20. Altshuler D
    21. Groop LC

    (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes

    Nature Genetics 34:267–273.

    https://doi.org/10.1038/ng1180

    • PubMed
    • Google Scholar
27. 1. Munn DH
    2. Sharma MD
    3. Hou D
    4. Baban B
    5. Lee JR
    6. Antonia SJ
    7. Messina JL
    8. Chandler P
    9. Koni PA
    10. Mellor AL

    (2004) Expression of indoleamine 2,3-dioxygenase by plasmacytoid dendritic cells in tumor-draining lymph nodes

    The Journal of Clinical Investigation 114:280–290.

    https://doi.org/10.1172/JCI21583

    • PubMed
    • Google Scholar
28. 1. Nagaharu K
    2. Kojima Y
    3. Hirose H
    4. Minoura K
    5. Hinohara K
    6. Minami H
    7. Kageyama Y
    8. Sugimoto Y
    9. Masuya M
    10. Nii S
    11. Seki M
    12. Suzuki Y
    13. Tawara I
    14. Shimamura T
    15. Katayama N
    16. Nishikawa H
    17. Ohishi K

    (2022) A bifurcation concept for B-lymphoid/plasmacytoid dendritic cells with largely fluctuating transcriptome dynamics

    Cell Reports 40:111260.

    https://doi.org/10.1016/j.celrep.2022.111260

    • PubMed
    • Google Scholar
29. 1. Nikolic T
    2. Dingjan GM
    3. Leenen PJM
    4. Hendriks RW

    (2002) A subfraction of B220(+) cells in murine bone marrow and spleen does not belong to the B cell lineage but has dendritic cell characteristics

    European Journal of Immunology 32:686–692.

    https://doi.org/10.1002/1521-4141(200203)32:3<686::AID-IMMU686>3.0.CO;2-I

    • PubMed
    • Google Scholar
30. Preprint

    1. Oskam N
    2. den Boer MA
    3. Lukassen MV
    4. Ooijevaar-de Heer P
    5. Veth TS
    6. van Mierlo G
    7. Lai SH
    8. Derksen NIL
    9. Yin V
    10. Streutker M
    11. Franc V
    12. Šiborová M
    13. Damen MJA
    14. Kos D
    15. Barendregt A
    16. Bondt A
    17. van Goudoever JB
    18. de Haas CJC
    19. Aerts PC
    20. Muts RM
    21. Rooijakkers SHM
    22. Vidarsson G
    23. Rispens T
    24. Heck AJR

    (2023) CD5L is a canonical component of circulatory IgM

    bioRxiv.

    https://doi.org/10.1101/2023.05.27.542462

    • Google Scholar
31. 1. Pelayo R
    2. Hirose J
    3. Huang J
    4. Garrett KP
    5. Delogu A
    6. Busslinger M
    7. Kincade PW

    (2005) Derivation of 2 categories of plasmacytoid dendritic cells in murine bone marrow

    Blood 105:4407–4415.

    https://doi.org/10.1182/blood-2004-07-2529

    • PubMed
    • Google Scholar
32. 1. Qiu X
    2. Mao Q
    3. Tang Y
    4. Wang L
    5. Chawla R
    6. Pliner HA
    7. Trapnell C

    (2017) Reversed graph embedding resolves complex single-cell trajectories

    Nature Methods 14:979–982.

    https://doi.org/10.1038/nmeth.4402

    • PubMed
    • Google Scholar
33. 1. Reizis B
    2. Bunin A
    3. Ghosh HS
    4. Lewis KL
    5. Sisirak V

    (2011) Plasmacytoid dendritic cells: recent progress and open questions

    Annual Review of Immunology 29:163–183.

    https://doi.org/10.1146/annurev-immunol-031210-101345

    • PubMed
    • Google Scholar
34. 1. Rodrigues PF
    2. Alberti-Servera L
    3. Eremin A
    4. Grajales-Reyes GE
    5. Ivanek R
    6. Tussiwand R

    (2018) Distinct progenitor lineages contribute to the heterogeneity of plasmacytoid dendritic cells

    Nature Immunology 19:711–722.

    https://doi.org/10.1038/s41590-018-0136-9

    • PubMed
    • Google Scholar
35. 1. Rönnblom L
    2. Pascual V

    (2008) The innate immune system in SLE: type I interferons and dendritic cells

    Lupus 17:394–399.

    https://doi.org/10.1177/0961203308090020

    • PubMed
    • Google Scholar
36. 1. Sakaguchi N
    2. Kashiwamura S
    3. Kimoto M
    4. Thalmann P
    5. Melchers F

    (1988) B lymphocyte lineage-restricted expression of mb-1, a gene with CD3-like structural properties

    The EMBO Journal 7:3457–3464.

    https://doi.org/10.1002/j.1460-2075.1988.tb03220.x

    • PubMed
    • Google Scholar
37. 1. Sanjurjo L
    2. Amézaga N
    3. Aran G
    4. Naranjo-Gómez M
    5. Arias L
    6. Armengol C
    7. Borràs FE
    8. Sarrias MR

    (2015) The human CD5L/AIM-CD36 axis: A novel autophagy inducer in macrophages that modulates inflammatory responses

    Autophagy 11:487–502.

    https://doi.org/10.1080/15548627.2015.1017183

    • PubMed
    • Google Scholar
38. 1. Sathe P
    2. Vremec D
    3. Wu L
    4. Corcoran L
    5. Shortman K

    (2013) Convergent differentiation: myeloid and lymphoid pathways to murine plasmacytoid dendritic cells

    Blood 121:11–19.

    https://doi.org/10.1182/blood-2012-02-413336

    • PubMed
    • Google Scholar
39. 1. Schmid ET
    2. Pang IK
    3. Carrera Silva EA
    4. Bosurgi L
    5. Miner JJ
    6. Diamond MS
    7. Iwasaki A
    8. Rothlin CV

    (2016) AXL receptor tyrosine kinase is required for T cell priming and antiviral immunity

    eLife 5:e12414.

    https://doi.org/10.7554/eLife.12414

    • PubMed
    • Google Scholar
40. 1. Shapiro-Shelef M
    2. Calame K

    (2005) Regulation of plasma-cell development

    Nature Reviews. Immunology 5:230–242.

    https://doi.org/10.1038/nri1572

    • PubMed
    • Google Scholar
41. 1. Shigematsu H
    2. Reizis B
    3. Iwasaki H
    4. Mizuno S
    5. Hu D
    6. Traver D
    7. Leder P
    8. Sakaguchi N
    9. Akashi K

    (2004) Plasmacytoid dendritic cells activate lymphoid-specific genetic programs irrespective of their cellular Origin

    Immunity 21:43–53.

    https://doi.org/10.1016/j.immuni.2004.06.011

    • Google Scholar
42. 1. Sisirak V
    2. Ganguly D
    3. Lewis KL
    4. Couillault C
    5. Tanaka L
    6. Bolland S
    7. D’Agati V
    8. Elkon KB
    9. Reizis B

    (2014) Genetic evidence for the role of plasmacytoid dendritic cells in systemic lupus erythematosus

    The Journal of Experimental Medicine 211:1969–1976.

    https://doi.org/10.1084/jem.20132522

    • PubMed
    • Google Scholar
43. 1. Swiecki M
    2. Colonna M

    (2015) The multifaceted biology of plasmacytoid dendritic cells

    Nature Reviews. Immunology 15:471–485.

    https://doi.org/10.1038/nri3865

    • PubMed
    • Google Scholar
44. 1. Thomas PD
    2. Campbell MJ
    3. Kejariwal A
    4. Mi H
    5. Karlak B
    6. Daverman R
    7. Diemer K
    8. Muruganujan A
    9. Narechania A

    (2003) PANTHER: a library of protein families and subfamilies indexed by function

    Genome Research 13:2129–2141.

    https://doi.org/10.1101/gr.772403

    • PubMed
    • Google Scholar
45. 1. Trapnell C
    2. Hendrickson DG
    3. Sauvageau M
    4. Goff L
    5. Rinn JL
    6. Pachter L

    (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq

    Nature Biotechnology 31:46–53.

    https://doi.org/10.1038/nbt.2450

    • PubMed
    • Google Scholar
46. 1. Trapnell C
    2. Cacchiarelli D
    3. Grimsby J
    4. Pokharel P
    5. Li S
    6. Morse M
    7. Lennon NJ
    8. Livak KJ
    9. Mikkelsen TS
    10. Rinn JL

    (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

    Nature Biotechnology 32:381–386.

    https://doi.org/10.1038/nbt.2859

    • Google Scholar
47. 1. Villani A-C
    2. Satija R
    3. Reynolds G
    4. Sarkizova S
    5. Shekhar K
    6. Fletcher J
    7. Griesbeck M
    8. Butler A
    9. Zheng S
    10. Lazo S
    11. Jardine L
    12. Dixon D
    13. Stephenson E
    14. Nilsson E
    15. Grundberg I
    16. McDonald D
    17. Filby A
    18. Li W
    19. De Jager PL
    20. Rozenblatt-Rosen O
    21. Lane AA
    22. Haniffa M
    23. Regev A
    24. Hacohen N

    (2017) Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

    Science 356:eaah4573.

    https://doi.org/10.1126/science.aah4573

    • PubMed
    • Google Scholar
48. 1. Wang YH
    2. Liu YJ

    (2004) Mysterious origin of plasmacytoid dendritic cell precursors

    Immunity 21:1–2.

    https://doi.org/10.1016/j.immuni.2004.07.003

    • PubMed
    • Google Scholar
49. 1. Wolf FA
    2. Hamey FK
    3. Plass M
    4. Solana J
    5. Dahlin JS
    6. Göttgens B
    7. Rajewsky N
    8. Simon L
    9. Theis FJ

    (2019) PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells

    Genome Biology 20:59.

    https://doi.org/10.1186/s13059-019-1663-x

    • PubMed
    • Google Scholar
50. 1. Wu X
    2. Satpathy AT
    3. Kc W
    4. Liu P
    5. Murphy TL
    6. Murphy KM

    (2013) BCL11A controls Flt3 expression in early hematopoietic progenitors and is required for pDC development in vivo

    PLOS ONE 8:e64800.

    https://doi.org/10.1371/journal.pone.0064800

    • PubMed
    • Google Scholar
51. 1. Yang GX
    2. Lian ZX
    3. Kikuchi K
    4. Moritoki Y
    5. Ansari AA
    6. Liu YJ
    7. Ikehara S
    8. Gershwin ME

    (2005) Plasmacytoid dendritic cells of different origins have distinct characteristics and function: studies of lymphoid progenitors versus myeloid progenitors

    Journal of Immunology 175:7281–7287.

    https://doi.org/10.4049/jimmunol.175.11.7281

    • PubMed
    • Google Scholar
52. 1. Yu Y
    2. Wang J
    3. Khaled W
    4. Burke S
    5. Li P
    6. Chen X
    7. Yang W
    8. Jenkins NA
    9. Copeland NG
    10. Zhang S
    11. Liu P

    (2012) BCL11A is essential for lymphoid development and negatively regulates p53

    Journal of Experimental Medicine 209:2467–2483.

    https://doi.org/10.1084/jem.20121846

    • Google Scholar
53. 1. Zhang H
    2. Gregorio JD
    3. Iwahori T
    4. Zhang X
    5. Choi O
    6. Tolentino LL
    7. Prestwood T
    8. Carmi Y
    9. Engleman EG

    (2017) A distinct subset of plasmacytoid dendritic cells induces activation and differentiation of B and T lymphocytes

    PNAS 114:1988–1993.

    https://doi.org/10.1073/pnas.1610630114

    • Google Scholar
54. 1. Zhang D
    2. Zhao Y
    3. Wang L
    4. You X
    5. Li J
    6. Zhang G
    7. Hou Y
    8. Wang H
    9. He S
    10. Li E

    (2022) Axl mediates resistance to respiratory syncytial virus infection independent of cell attachment

    American Journal of Respiratory Cell and Molecular Biology 67:227–240.

    https://doi.org/10.1165/rcmb.2021-0362OC

    • PubMed
    • Google Scholar

Author details

1. Alessandra Machado Araujo

   Department of Chemical Engineering, The University of Texas at Austin, Austin, United States

   Contribution

   Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Joseph D Dekker

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7568-074X

2. Joseph D Dekker

   Department of Chemical Engineering, The University of Texas at Austin, Austin, United States

   Contribution

   Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Alessandra Machado Araujo

   For correspondence

   joedekker@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2068-3529

3. Kendra Garrison

   Department of Chemical Engineering, The University of Texas at Austin, Austin, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Zhe Su

   Department of Biomedical Engineering, and Livestrong Cancer Institutes, The University of Texas at Austin, Austin, United States

   Contribution

   Software, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Catherine Rhee

   Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Zicheng Hu

   Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Bum-Kyu Lee

   Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Daniel Osorio

   Department of Biomedical Engineering, and Livestrong Cancer Institutes, The University of Texas at Austin, Austin, United States

   Contribution

   Resources, Methodology

   Competing interests

   No competing interests declared

9. Jiwon Lee

   Department of Chemical Engineering, The University of Texas at Austin, Austin, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Vishwanath R Iyer

    Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3383-248X

11. Lauren IR Ehrlich

    Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States

    Contribution

    Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1697-1755

12. George Georgiou

    Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States

    Contribution

    Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

13. Gregory Ippolito

    Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States

    Contribution

    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

14. Stephen Yi

    Department of Biomedical Engineering, and Livestrong Cancer Institutes, The University of Texas at Austin, Austin, United States

    Contribution

    Supervision, Methodology

    For correspondence

    stephen.yi@austin.utexas.edu

    Competing interests

    No competing interests declared

15. Haley O Tucker

    Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States

    Contribution

    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    haleytucker@austin.utexas.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7735-2862

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